Book Chapter
First
1
2026
Salomon, J., Winnewisser, N. R., & Beer, M. (2026). Resilience Decision- Making under Climate Hazards for Complex and Substructured Infrastructure Systems. In Climate-Resilient Structures and Infrastructures (pp. 46-78). CRC Press. https://doi.org/10.1201/9781003567332-3
Svitek, M., Winnewisser, N., Beer, M., Kosheleva, O., & Kreinovich, V. (2026). Why Shapley Value and Its Generalizations Are Effective in Economics and Finance, Machine Learning, and Systems Engineering. In Studies in Big Data (pp. 15-26). (Studies in Big Data; Vol. 181). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-06179-9_2
2025
Salomon, J., Broggi, M., & Beer, M. (2025). Resilience-Based Decision Criteria for Optimal Regeneration. In Regeneration of Complex Capital Goods: Contributions to the Final Symposium of the Collaborative Research Center 871 (pp. 393-422) https://doi.org/10.1007/978-3-031-51395-4_20
2024
Elias, S., & Beer, M. (2024). SEISMIC PERFORMANCE OF MODULAR BUILDLING UNDER NEAR FIELD GROUND MOTIONS. In World Conference on Earthquake Engineering proceedings (World Conference on Earthquake Engineering proceedings; Vol. 2024). International Association for Earthquake Engineering. Advance online publication.
Tyrsin, A. N., Kashcheev, S. E., Beer, M., Kashcheev, S. E., & Gerget, O. M. (2024). Entropy Indicators of Cascading Failures Risk in Gaussian Interconnected Network Structures. In Cyber-Physical Systems: Industry 4.0 to Industry 5.0 Transition (pp. 219-234). (Studies in Systems, Decision and Control; Vol. 560). https://doi.org/10.1007/978-3-031-67911-7_17
2023
Beer, M. (2023). Fuzzy Probability Theory. In Granular, Fuzzy, and Soft ComputingTsau: A Volume in the Encyclopedia ofComplexity and Systems Science,Second Edition (pp. 51–75). (Encyclopedia of Complexity and Systems Science Series). https://doi.org/10.1007/978-1-0716-2628-3_237
Chen, Y., Patelli, E., Edwards, B., & Beer, M. (2023). Spectral Density Estimation Of Stochastic Processes Under Missing Data And Uncertainty Quantification With Bayesian Deep Learning. In Ecomas Proceedia UNCECOMP 2023 (International Conference on Uncertainty Quantification in Computational Science and Engineering; Vol. 5). National Technical University of Athens. https://doi.org/10.7712/120223.10371.19949
Galindo, O., Ibarra, C., Kreinovich, V., & Beer, M. (2023). Fourier Transform and Other Quadratic Problems Under Interval Uncertainty. In Decision Making Under Uncertainty and Constraints (pp. 251-256). (Studies in Systems, Decision and Control; Vol. 217). Springer Verlag. https://doi.org/10.1007/978-3-031-16415-6_37
Jerez, D. J., Jensen, H. A., & Beer, M. (2023). A Two-Phase Sampling Approach for Reliability-Based Optimization in Structural Engineering. In Advances in Reliability and Maintainability Methods and Engineering Applications: Essays in Honor of Professor Hong-Zhong Huang on his 60th Birthday (pp. 21-48). (Springer Series in Reliability Engineering; Vol. Part F266). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-28859-3_2
Wei, P., & Beer, M. (2023). Regression Models for Machine Learning. In Machine Learning in Modeling and Simulation : Methods and Applications (Computational Methods in Engineering & the Sciences (CMES) ). https://doi.org/10.1007/978-3-031-36644-4_9
2022
Bi, S., & Beer, M. (2022). Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment. In Uncertainty in Engineering. SpringerBriefs in Statistics (pp. 115-130). (Uncertainty in Engineering. SpringerBriefs in Statistics (BRIEFSSTATIST)). https://doi.org/10.1007/978-3-030-83640-5_8
2017
Neumann, I., Beer, M., Gong, Z., Sriboonchitta, S., & Kreinovich, V. (2017). What if we do not know correlations? In Studies in Computational Intelligence (pp. 78-85). (Studies in Computational Intelligence; Vol. 760). Springer Verlag. https://doi.org/10.1007/978-3-319-73150-6_5
2012
Beer, M. (2012). Fuzzy probability theory. In R. A. Meyers (Ed.), Computational Complexity: Theory, Techniques, and Applications (pp. 1240-1252). Springer New York. https://doi.org/10.1007/978-1-4614-1800-9_76
2003
Möller, B., Graf, W., Beer, M., & Sickert, J. U. (2003). Fuzzy stochastic finite element method. In Computational Fluid and Solid Mechanics 2003 (pp. 2074-2077). Elsevier Inc.. https://doi.org/10.1016/B978-008044046-0.50509-1
First
1
Journal Articles
2026
Behrendt, M., Fragkoulis, V. C., Pasparakis, G. D., & Beer, M. (2026). Probabilistic failure analysis of stochastically excited nonlinear structural systems with fractional derivative elements. Reliability Engineering and System Safety, 266, Article 111647. https://doi.org/10.1016/j.ress.2025.111647
Bittner, M., Grashorn, J., Keßler, S., & Beer, M. (2026). Unsupervised anomaly detection in non-linear mechanical systems for structural health monitoring. Acta Mechanica Sinica/Lixue Xuebao, 42(8), Article 725585. https://doi.org/10.1007/s10409-025-25585-x
Chen, J., Wu, J., Guo, L., Shan, Y., & Beer, M. (2026). Preserving the microstructural fabric integrity of sand: A non-invasive gelatin-based hydrogel stabilization method. Powder technology, 469, Article 121891. https://doi.org/10.1016/j.powtec.2025.121891
Ding, J. Y., Feng, L., Cao, X. Y., Feng, D. C., & Beer, M. (2026). Quantification of model uncertainties in regional-scale seismic analysis of building portfolios. Mechanical Systems and Signal Processing, 248, Article 113990. https://doi.org/10.1016/j.ymssp.2026.113990
Fritsch, L., Geisler, H., Grashorn, J., Klempt, F., Soleimani, M., Broggi, M., Junker, P., & Beer, M. (2026). Bayesian Updating of constitutive parameters under hybrid uncertainties with a novel surrogate model applied to biofilms. Computational mechanics. Advance online publication. https://doi.org/10.1007/s00466-026-02777-8
He, J., Gao, R., Feng, D. C., & Beer, M. (2026). A Bayesian compressive sensing: Stochastic harmonic function based random field simulation method incorporating with Gaussian mixture model to consider fusion of multi-groups of spatial data. Mechanical Systems and Signal Processing, 251, Article 114199. https://doi.org/10.1016/j.ymssp.2026.114199
He, W., Wang, C., Li, Y., & Beer, M. (2026). A Bayesian polynomial chaos neural network for the analysis and design of a Y-frame core sandwich panel. Engineering with computers, 42(3), Article 108. https://doi.org/10.1007/s00366-026-02349-7
Hong, F., Wei, P., Xu, H., & Beer, M. (2026). Active and collaborative Bayesian calibration of model parameters and bias. Mechanical Systems and Signal Processing, 244, Article 113817. https://doi.org/10.1016/j.ymssp.2025.113817
Hu, Z., Wang, D., Dang, C., Beer, M., & Wang, L. (2026). Uncertainty-aware adaptive Bayesian inference method for structural time-dependent reliability analysis. Mechanical Systems and Signal Processing, 256, Article 114476. https://doi.org/10.1016/j.ymssp.2026.114476
Huang, T., Zhang, Q., Beer, M., Liu, Y., & Huang, H. Z. (2026). A dynamic reliability assessment method for multi-state manufacturing system by merging imprecise observational information. Reliability Engineering and System Safety, 266, Article 111722. https://doi.org/10.1016/j.ress.2025.111722
Huang, Z., Zuev, K. M., Xia, Y., & Beer, M. (2026). Upper approximation bounds for neural oscillators. Mechanical Systems and Signal Processing, 256, Article 114485. https://doi.org/10.1016/j.ymssp.2026.114485
Li, N., Hu, X., Huang, J., Beer, M., & Zheng, H. (2026). Adaptive portfolio optimization-based metamodel method for the multi-armed bandit problem of learning function in slope reliability analysis. Reliability Engineering and System Safety, 271, Article 112168. https://doi.org/10.1016/j.ress.2025.112168
Li, P. P., Valdebenito, M. A., Dang, C., Beer, M., & Faes, M. G. R. (2026). Aleatory and epistemic uncertainty in reliability analysis: An engineering perspective. Structural safety, 119, Article 102666. https://doi.org/10.1016/j.strusafe.2025.102666
Li, Y., Duan, Y., He, W., & Beer, M. (2026). An effective approach for uncertainty analysis of power spectral density of stochastic vibration problems via B-spline theory and maximum entropy method. Structural and Multidisciplinary Optimization, 69(1), Article 19. https://doi.org/10.1007/s00158-025-04230-5
Li, N., Hu, X., & Beer, M. (2026). A quantitative assessment framework for the physical vulnerability of building to subaerial landslide-induced impulse wave. Engineering structures, 360, Article 122583. https://doi.org/10.1016/j.engstruct.2026.122583
Li, Y., Cao, X. Y., Feng, D. C., & Beer, M. (2026). Loss-based seismic retrofitting design for existing RC structures through loss-displacement demand transition. Bulletin of earthquake engineering, 24(8), 6217-6253. https://doi.org/10.1007/s10518-026-02501-0
Li, X. Q., Li, Q. S., Faes, M. G. R., & Beer, M. (2026). Multi-failure reliability evaluation of wind turbine blades under extreme conditions. Structures, 88, Article 111836. https://doi.org/10.1016/j.istruc.2026.111836
Li, S. Q., Beer, M., & Gardoni, P. (2026). Probabilistic seismic risk and vulnerability assessment framework for RC buildings considering the influence of mainshock-aftershock sequences. Probabilistic Engineering Mechanics, 84, Article 103921. https://doi.org/10.1016/j.probengmech.2026.103921
Li, X. Y., Wang, H., Li, H., Xiong, X., Yang, Z., Huang, H. Z., Beer, M., & Wang, J. (2026). Reliability-Based Maintenance Optimization of Reusable Phased Mission Systems. Reliability Engineering and System Safety, 270, Article 112187. https://doi.org/10.1016/j.ress.2026.112187
Lu, N., Cui, J., Zeng, W., Liu, Y., & Beer, M. (2026). Damage detection of a cable-stayed bridge specimen based on unsupervised subdomain adaptation transfer learning. Engineering structures, 346, Article 121660. https://doi.org/10.1016/j.engstruct.2025.121660
